Ecology, 90(3), 2009, pp. 767–780
Ó 2009 by the Ecological Society of America
Defining fundamental niche dimensions of corals:
synergistic effects of colony size, light, and flow
MIA O. HOOGENBOOM1
AND
SEAN R. CONNOLLY
ARC Centre of Excellence for Coral Reef Studies and School of Marine and Tropical Biology, James Cook University,
Townsville, Queensland 4811 Australia
Abstract. The ‘‘fundamental niche’’ is the range of conditions under which an organism
can survive and reproduce, measured in the absence of biotic interactions. Niche
measurements are often based on statistical relationships between species presence and
measured environmental variables, or inferred from measured responses of species along
hypothesized niche axes. In this study, we use novel, process-based models of how irradiance
and gas diffusion influence photosynthesis and respiration to predict niche dimensions for
three coral species: Acropora nasuta, Montipora foliosa, and Leptoria phrygia. We use a
combination of mathematical modeling, laboratory experiments, and field observations to
establish the link between energy acquisition and the dominant environmental gradients on
reefs: light intensity and water flow velocity. Our approach allows us to quantify how the
shape of the niche varies in response to light and flow conditions. The model predicts that, due
to its higher photosynthetic capacity, the branching coral A. nasuta has a positive energy
balance over a wider range of conditions than both a massive species (L. phrygia) and a foliose
species (M. foliosa). Moreover, colony size influences niche width, with larger colonies of all
three species achieving a positive energy balance over a broader range of conditions than small
colonies. Comparison of model predictions with field data demonstrated that tissue biomass
and reproductive output are significantly and positively correlated with predicted energy
acquisition. These results show how interactions between light and flow determine organism
performance along environmental gradients on coral reefs. In addition, this study
demonstrates the utility of process-based models for quantifying how physiology influences
ecology, and for predicting the ecological consequences of varying environmental conditions.
Key words: Acropora nasuta; carbon acquisition; fundamental niche; Leptoria phrygia; light intensity;
mass transfer coefficient; Montipora foliosa; photosynthesis; physiological model; scleractinian corals; water
flow velocity.
INTRODUCTION
The niche is an important concept in ecology that
allows consideration of the roles of biotic and abiotic
processes in shaping species distributions (e.g., Connell
1961). Although this term carries many different
meanings (e.g., Whittaker et al. 1973, Chase and Leibold
2003), the ‘‘fundamental niche’’ was initially defined as
the range of conditions under which an organism can
survive and reproduce in the absence of biotic interactions (e.g., Hutchinson 1957). Over time, methods for
measuring the niche have evolved from a conceptual
‘‘hypervolume’’ (Hutchinson 1957), to statistical techniques that identify environmental variables that are
strong correlates of species occurrence (e.g., Austin et al.
1990, Wright et al. 2006). Another approach is to
experimentally quantify organism performance (e.g.,
growth) along hypothesized niche axes (e.g., Greulich
Manuscript received 7 December 2007; revised 27 May 2008;
accepted 16 June 2008. Corresponding Editor: R. B. Aronson.
1 Present address: Centre Scientifique de Monaco, Avenue
St. Martin, MC98000 Monaco.
E-mail: mia@centrescientifique.mc
et al. 2000, Antoine and McCune 2004). Although these
techniques identify correlates of species occurrence, they
do not characterize the processes that underlie such
relationships. An alternative is to describe the niche
based on the underlying mechanisms that determine how
specific environmental conditions influence organism
performance. Although there are few examples of this
approach, in one such effort, Kearney and Porter (2004)
combined physiological measurements, biophysical
models, and climate data to describe the fundamental
niche of a lizard. Mechanistic approaches to niche
studies are beneficial because they explicitly identify
drivers of species’ distributions, and allow greater
confidence when extrapolating niche properties to
conditions not included in multivariate data sets (Pulliam 2000, Kearney 2006).
An additional complication for niche measurement is
that over the course of an organism’s life, changes in
body size can alter how the environment influences
individuals. These ontogenetic changes are most commonly observed in ‘‘trophic niches’’: for example, as
predators grow larger, their prey selection changes (e.g.,
Scharf et al. 2000). However, such effects are also likely
767
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MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
to be evident in the niche of sessile organisms,
particularly because they are committed to their site of
settlement irrespective of any unfavorable conditions
that may prevail as they grow larger. Although
investigations of this kind are rare, rain forest plants
occur under a broader range of conditions as juveniles
than as adults (Webb and Peart 2000). Similarly,
correlative approaches to niche measurement indicate
that, for some taxa, the presence of juveniles may be
predicted by a different set of environmental variables
than that identified for adults (e.g., tree ferns [Jones et
al. 2007]).
Physiology and energy balance are fundamental to
organism performance (Kooijman 2000, Kearney 2006).
Therefore, the axes delimiting the fundamental niche
should include those variables that influence energy and
mass balance (Kearney and Porter 2004). For photosynthetic organisms, generating energy for growth and
reproduction requires light to activate the photosynthetic machinery, together with exchange of CO2 and O2
gases during carbon fixation and metabolism (e.g.,
Falkowski and Raven 1997). Despite the existence of
general principles that describe diffusion of gases from
smooth surfaces, such models do not include light
intensity as a factor influencing photosynthesis under
natural conditions (e.g., Patterson et al. 1991). Therefore, although it is clear that the niche is strongly
influenced by both light intensity and gas exchange, the
interactive effects of these processes on the size and
location of the fundamental niche are unknown. The
primary aim of this study was to develop a model of
energy acquisition as a function of light, gas exchange,
and organism size, and to use this framework to describe
the fundamental niche of three coral species.
Reef-building corals inhabit heterogeneous environments in which light intensity and water flow (i.e.,
potential gas exchange) decrease with depth (Helmuth et
al. 1997a). Therefore, corals are appropriate model
organisms for developing mechanistic hypotheses about
the relationships between environmental gradients and
the fundamental niche. In this study, we use a
combination of mathematical modeling, laboratory
experiments, and field observations to model the niche.
Under Hutchinson’s definition of the fundamental
niche, identifying the conditions under which species
are able to survive and reproduce requires knowledge of
environmental effects on birth and death rates. In some
cases, such rates cannot be readily quantified. For
example, for organisms with highly dispersive offspring,
such as corals, the fraction of offspring produced by an
individual that survive to establish as juveniles cannot be
reliably determined. In these cases, physiological or
energetic proxies of the capacity for population growth
are often used (e.g., Anthony and Connolly 2004,
Kearney and Porter 2004). We here define the niche as
the conditions under which individuals have a positive
energy balance. In other words, we delineate the
conditions under which individuals are able to survive,
Ecology, Vol. 90, No. 3
and have a surplus of energy that may be allocated to
reproduction. This definition of the niche is based on a
central concept of ecophysiology and life-history theory:
that all organisms require a net gain in energy for
growth, reproduction, and survival to be possible
(Kooijman 2000). Specifically, we aimed to: (1) measure
niche width along gradients of light and flow for
different coral species; and (2) explicitly account for
colony size as a potential factor influencing niche size.
To assess the predictive accuracy of this model we
compared predicted energy acquisition with measures of
tissue quality and reproduction for colonies of known
size, sampled from field sites where light and flow were
also quantified.
METHODS
Modeling framework
Corals are symbiotic organisms that obtain the
majority of their energy (carbon) from photosynthesis
(e.g., Muscatine et al. 1981). In all environments,
photosynthetic gas exchange occurs across a diffusive
boundary layer (DBL, i.e., a layer of stagnant fluid
adjacent to the photosynthesizing surface [Nobel 1983]).
The rate at which molecules diffuse across DBLs
depends upon the concentration of molecules on either
side of the layer, and the overall thickness of the layer
(Fick’s laws [Nobel 1983]). In turn, DBL thickness
depends upon fluid velocity and turbulence (Nobel 1983,
Denny 1988). Based on this principle, general models of
gas exchange have been developed. These models relate
photosynthesis to water flow using nondimensional
parameters (Reynolds number, Re, and Sherwood
number, Sh) to quantify the extent to which mass flux
is enhanced by fluid motion relative to that possible
through diffusion (e.g., Helmuth et al. 1997a, Falter et
al. 2007). Re summarizes the ratio of inertial to viscous
forces of the fluid, and characterizes flow over a surface
(Denny 1988):
Re ¼
uW
v
ð1Þ
where u is flow velocity, W is organism size (e.g., length
in the direction of flow) and v is the kinematic viscosity
of the fluid. Sh indicates how much mass flux is assisted
by direct transfer (advection) relative to the potential
flux if diffusion was the only mechanism operating
(Denny 1988, Patterson 1992), and is given by
Sh ¼
hm W
D
ð2Þ
where hm is the mass transfer coefficient, and D is the
diffusion coefficient (i.e., the time taken for the gas to
diffuse a set distance [Denny 1988]). The relationship
between Sh and Re has been well characterized
experimentally, as
Sh ¼ aReb
ð3Þ
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QUANTIFYING THE FUNDAMENTAL NICHE
769
TABLE 1. Definitions of mass transport parameters.
Parameter
v
D
u
hm
a and b
Pvs
Rvs
Pvf
P
Pday
xP and xR
aP and aR
bP and bR
E
EK
Emax
Definition (units)
Estimate and source
2
Kinematic viscosity of seawater (cm /s)
Molecular diffusivity of O2 in seawater (cm2/s)
Flow velocity (cm/s)
Mass-transfer coefficient (cm/s)
Coefficients of relationship between Sh:Re (dimensionless)
Oxygen concentration at tissue surface during maximum
photosynthesis (%O2)
Oxygen concentration at tissue surface during respiration in
darkness (%O2)
O2 concentration within seawater (%O2)
O2 flux from tissue (%O2 cm2s1)
Daily integrated energy acquisition (%O2cm2d1)
Deviation of asymptotic oxygen tissue concentration from
100% during photosynthesis (xP) and respiration (xR, %O2)
Rate at which Pvs approaches saturation with increasing Re during
photosynthesis (aP) or respiration (aR) (%O2/Re)
Maximum (bP) or minimum (bR) tissue oxygen concentration as Re
approaches zero (%O2)
Light intensity (lmol photonsm2s1)
Subsaturation irradiance (lmol photonsm2s1)
Maximum daily irradiance (lmol photonsm2s1)
where a and b are empirically determined coefficients
that depend upon organism shape and surface roughness. Analysis of the relationship between Sh and Re
allows the effects on gas exchange of organism size and
flow velocity to be investigated. This is achieved by
rearranging Eqs. 2 and 3 to obtain an expression for the
mass transfer coefficient, hm:
hm ¼
aReb D
W
ð4Þ
where a, b, D, and W are as just defined (see Table 1). By
definition, hm summarizes the interaction of organism
morphology with fluid motion (Patterson 1992). Therefore, combining this expression for hm (Eq. 4) into a
general gas exchange model allows the effects on
photosynthesis of colony size, colony morphology and
flow velocity to be investigated:
P ¼ hm AðPvs Pvf Þ
ð5Þ
where P is the rate of gas exchange (in this case,
photosynthetic oxygen flux), hm is the mass transfer
coefficient, Pvf and Pvs are the concentrations of gas in
the fluid and at the photosynthesizing surface, respectively, and A is the surface area.
Accurate quantification of the coefficients of the
Sh:Re relationship (a and b in Eqs. 3 and 4) is critical
for analyses of the effects of flow on photosynthesis.
Engineering theory provides estimates of these coefficients for regularly shaped, smooth structures such as
cylinders and spheres (see Helmuth et al. 1997b).
However, for rugose, irregular structures like corals,
these parameters must be determined empirically. While
several studies have explored the relationship between Sh
and Re for corals, estimates of a and b based on live
tissue measurements are only available for two species:
Montastrea annularis (Patterson et al. 1991) and Pocillo-
2
10 (Patterson et al. 1991)
2 3 105 (Patterson et al. 1991)
variable
variable (light and flow dependent)
Fig. 1
variable (light and flow dependent)
variable (light and flow dependent)
see Methods: Modeling framework
variable
variable
Table 2
Table 2
Table 2
variable
100
variable
pora damicornis (Lesser et al. 1994). Other studies have
instead explored mass-flux dynamics by measuring water
evaporation from coral skeletons in a wind tunnel (e.g.,
Helmuth et al. 1997a, b), or the flux of calcium carbonate
from plaster-covered skeletons (Falter et al. 2007).
In this study, we extended the general model outlined
above in two ways. First, we improved on existing
methods for calculating the mass transfer coefficient
(hm). Suitable equipment for measuring tissue-surface
oxygen concentrations (Pvs) was only developed recently. For this reason, previous studies evaluated hm by
setting Pvs ¼ 0 in darkness, and assuming Pvs is ‘‘at
saturation’’ when colonies are photosynthesizing (Patterson and Sebens 1989, Patterson et al. 1991). However,
recent studies have found tissue oxygen concentrations
between 10% and 91% of saturation in darkness, and
between 107% and 400% of saturation during photosynthesis (Shashar et al. 1993, Gardella and Edmunds
1999, Larkum et al. 2003). To allow for such variation in
tissue oxygen concentrations, we calibrated the relationship between hm, flow, and colony size using simultaneous direct measurements of total oxygen flux from
colonies of known surface area, oxygen concentration in
the water column, and oxygen concentration at the
tissue surface (see Photosynthesis experiments).
Second, we extended the general model (Eq. 5) to
calculate daily energy acquisition (Pday) from rates of
photosynthesis integrated over the diurnal cycle, explicitly incorporating light intensity. Expressed in this way,
Eq. 5 becomes
Z 24
Pday ¼
hm ðu; WÞfPvs ½EðtÞ; u Pvf gdt
ð6Þ
t¼0
where the mass transfer coefficient h m (u, W ) is
calibrated experimentally as outlined above. To incorporate the dependence of photosynthesis on light
770
MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
intensity, we allowed Pvs(E(t), u) to follow the hyperbolic tangent equation: as light intensity increases, Pvs
rises from its minimum value in darkness (Rd) up to its
saturated value at Pmax. Both Rd and Pmax are
dependent upon flow velocity, u:
Pvs ½EðtÞ; u ¼ ½Pmax ðuÞ Rd ðuÞtanh
EðtÞ
þ Rd ðuÞ
Ek
For the mass flux measurements, we defined the
characteristic dimension of colonies (W, in centimeters)
as length in the direction of flow, and calculated this
length by photographing colonies using a fixed grid as a
scale bar, and analyzing photos using ImageTool
software (UTHSCSA, v2).2
ð7Þ
For these analyses, we set Pvf as a constant, equal to the
average value measured during our experiments. Finally,
we allowed light intensity to vary over the day as a sine
function defined by maximum daily irradiance (Emax),
time of day (t) and day length (L, 12 hours):
pt
:
ð8Þ
EðtÞ ¼ Emax sin2
L
Due to time constraints during data collection, we
were unable to obtain estimates of Ek (light intensity at
which photosynthesis is 75% of its maximum value). For
many corals, estimates of Ek lie between 50 and 400 lmol
photonsm2s1 (Chalker et al. 1983, Anthony and
Hoegh-Guldberg 2003a). We here assume a fixed value of
100 lmol photonsm2s1 as a baseline value suitable for
low-light-acclimated corals. However, to confirm that
our results are robust to this assumption, we explored the
sensitivity of our model to the value of Ek.
Study species and aquarium setup
Experimental work for this study was conducted in
May–June 2006 at One Tree Island (Great Barrier Reef
[GBR], Australia, 23828 0 S, 152804 0 E; see Plate 1). Data
collection and model calibration was implemented for
three species: Acropora nasuta (branching), Leptoria
phrygia (mound-shaped), and Montipora foliosa (plating). These species are common on the GBR and occur
across a range of light and flow conditions (Veron 2000).
For each species, 15–18 colonies measuring 15–20 cm
in diameter, were collected from 4- and 9-m depths and
maintained in a large aquarium (200 L) with continuous
water exchange. Thermostat-controlled aquarium heaters maintained water temperature between 188C and
208C, approximating water temperature in the field at
the time of collecting (19.48C). Light and temperature
within the tank were continuously monitored using
Odyssey loggers (Odyphoto and Odytemp, Data Flow
systems, Burnside Christchurch, New Zealand). Different light acclimation treatments were established by
dividing the aquarium into high- and low-light regions
(HL and LL, respectively) using shade cloth. Each
treatment had 7–9 colonies per species allocated to it,
with colonies from shallow depths placed under HL and
colonies from deeper water under LL. During the
experiments, average maximum irradiance in treatments
was 520 lmol photonsm2s1 (HL) and 250 lmol
photonsm2s1 (LL), which corresponds to depths of
approximately 7 m and 14 m in the field, respectively
(M. Hoogenboom, unpublished data). Colony surface
area was determined by foil-wrapping (Marsh 1970).
Ecology, Vol. 90, No. 3
Photosynthesis experiments
Respirometry.—Photosynthesis measurements were
conducted using oxygen respirometry chambers. Unidirectional, recirculating flow was generated within these
chambers using propellers driven by variable-speed 12-V
DC motors. This design allowed different flow speeds to
be produced by varying the power input to the motors.
Our analyses required the use of two different sizes of
respirometry chambers: a single small (10-L) chamber
was used to measure the Re:Sh relationship, and a set of
four large (19.5-L) chambers was used to measure rates
of photosynthesis and respiration across a wide range of
flow speeds. Water flow within chambers was calibrated
for all power settings using visual tracking of small,
neutrally buoyant wooden beads. To do this, series of
still images were captured from video footage of
circulating particles, the positions of randomly chosen
particles along the centerline of the chamber were
overlaid in a time series, and the distance moved by
each particle was calculated in both horizontal and
vertical directions. Both flow speed and water turbulence
can affect mass flux (e.g., Falter et al. 2007). Therefore,
because chamber size can influence turbulence, we
analyzed the relationship between flow speed and
turbulence in the small and large chambers to verify
that the flow regimes did not differ between chambers.
To do this, we plotted the relationship between mean
flow speed and the root mean square of the turbulent
fluctuations in flow speed and compared the slopes of
these relationships. These analyses confirmed that the
flow characteristics of the two sizes of chambers were
indeed consistent (see Appendix A for details).
Whole-colony rates of oxygen flux from live colonies
were determined by continuously measuring oxygen
concentration within chambers over a 45-minute measuring period. To do this, we used Clark-type oxygen
electrodes connected to a signal-linearizing device
(Cheshire Systems, Adelaide, Australia), and recorded
using a datalogger (CR10X, Campbell-Scientific, Thuringowa Central, Australia). Tissue surface (Pvs) and
free-stream (Pvf) oxygen concentrations were measured
using oxygen micro-optodes (PreSens, Regensburg,
Germany; 140 lm thick), that were positioned using a
micro-manipulator either in the water column well
above the colony (measuring Pvf) or directly at the
tissue surface (measuring Pvs). Optodes were connected
to a single-channel fiber-optic oxygen meter (Microx
TX, PreSens, Regensburg, Germany), with the signal
2
hhtml://ddsdx.uthscsa.edu/dig/itdesc.htmli
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QUANTIFYING THE FUNDAMENTAL NICHE
771
PLATE 1. One Tree Island reef is located in the southern section of the Great Barrier Reef in Australia and has been a site of
active research for over 40 years. The island is surrounded by an extensive and complex lagoon system. Environmental conditions,
particularly light intensity and water flow velocity, vary strongly between exposed outer reef margins and protected positions within
the lagoon. Photo credit: Michael Kingsford, courtesy of One Tree Island Research Station.
logged and monitored in real time (every three seconds)
using a laptop computer. Following each measuring
period, motors were turned off and chambers were
flushed with fresh seawater. Oxygen electrodes and
micro-optodes were calibrated immediately prior to and
after respirometry runs, using seawater at 100% O2
saturation, and nitrogen gas as a 0% O2 baseline.
Respirometry chambers were submerged in a 200-L
water jacket to ensure consistent temperature during
measuring periods. In addition, water temperature and
salinity were monitored twice daily to account for
potential variation in oxygen solubility across subsequent days of data collection.
Reynolds : Sherwood relationship.—To calibrate the
relationship between Reynolds and Sherwood number
(Eq. 3), simultaneous measurements of (a) oxygen
concentrations at the tissue surface, Pvs (b) free-stream
oxygen concentrations, Pvf, and (c) whole-colony rates
of photosynthesis, were carried out in a single respirometry chamber (10-L volume). Photosynthesis assays
were conducted at 2–3 flow speeds, for 3–4 colonies of
each species from each light-acclimation treatment.
These measurements were made at flow speeds between
2 and 10 cm/s, encompassing only those flow velocities
over which mass flux is limited by gas diffusion (i.e.,
below 10 cm/s; Hurd 2000). For each colony, Pvs and Pvf
were measured at 6–8 points on the colony surface, with
the measurement for each point calculated as the
average of 6 replicate measurements taken over a 20-s
time period.
Using the simultaneous measurements of rates of
photosynthesis and tissue-surface oxygen concentrations, we evaluated the mass transfer coefficient (hm)
as the ratio of average mass flux over the colony surface,
P, to the (average) oxygen concentration gradient (Pvs–
Pvf). Sh and Re were then determined from Eqs. 1 and 2
(see Table 1). The coefficients a and b of the Sh:Re
relationship were estimated from linear regression of
log10 values for these parameters using the software
package Statistica (v7, StatSoft, Tulsa, Oklahoma,
USA). To test for potential effects of photo-acclimatory
state on this relationship, we estimated these coefficients
separately for high- and low-light-acclimated colonies,
and then used t tests to determine whether the slopes of
these relationships differed across light treatments.
Effects of flow and colony size on photosynthesis and
respiration.—To calibrate the model over a greater range
of colony sizes and flow speeds, additional measurements of whole-colony rates of photosynthesis and
respiration were made for all corals at flows ranging
772
MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
between 6 and 38 cm/s. These values capture the range
of flow velocities commonly experienced in reef environments (e.g., Sebens and Johnson 1991, Helmuth et al.
1997a). For these assays, measurements were made
using the set of four large respirometry chambers. Each
day of data collection (18 days in total), four colonies
were selected at random, and maximum rates of
photosynthesis (Pmax) and dark-respiration (Rd) were
measured in a darkened room, where the only light
source was a set of 400-W metal halide lamps (EYE,
Tokyo, Japan). Using the procedure described in
Respirometry above, measurements of Pmax were carried
out at 3–6 flow speeds between 11:00 hours and 16:00
hours, with colonies exposed to a constant irradiance of
720 lmol photonsm2s1 during this period. This
irradiance was sufficient to saturate rates of photosynthesis for our corals (e.g., .500 lmol photonsm2s1;
see Chalker et al. 1983, Anthony and Hoegh-Guldberg
2003a, b) without causing a decline in rates of photosynthesis due to photoinhibition (e.g., Hoogenboom et
al. 2006). Following the photosynthesis measurements,
colonies were rested in darkness for 1.5 hours with
chambers continuously flushed with seawater. Rd was
subsequently measured at 3–6 flow speeds between 17:30
and 22:30 hours. To control for possible photosynthesis
and respiration of water-borne microorganisms, we
conducted control (empty-chamber) runs at each flow
speed, and subtracted the oxygen evolution/consumption by the water column from colony photosynthesis
and respiration data.
Measurements of photosynthesis and respiration were
used to account for a potential limitation of oxygen
concentrations within coral tissue by photosynthetic
activity (i.e., enzyme processing) rather than by diffusion
of gases from tissue. To do this, we used the speciesspecific calibrations of the dependence of hm on u and W
(the Sh:Re relationship) to calculate tissue oxygen
concentrations corresponding to the measurements of
Pmax and Rd for each colony. We then expressed tissue
oxygen concentration as a percentage relative to the
water column during photosynthesis (Pvs 4 Pvf 3 100 )
and respiration (Rvs 4 Pvf 3 100). We then fitted an
equation of the following form to Pvs and Rvs data for
each species:
%O2 ¼ ð100 þ xÞ þ bexpaRe
ð9Þ
where b is the percentage by which O2 at the tissue
surface deviates from saturation at Re ¼ 0, a is the rate
at which O2 saturation changes with increasing Re, and
x allows for an asymptote at values different from 100%.
Tissue quality and reproductive output
As an independent test of model performance, we
compared estimates of tissue quality and reproductive
output from colonies sampled in the field with model
predictions of energy acquisition. To do this, we
established 10 sites in and around One Tree Island
lagoon. These sites were stratified to encompass a range
Ecology, Vol. 90, No. 3
of light and flow conditions, and were spread between
the windward and leeward sides of the reef exterior, as
well as within the lagoon itself. At each site, light and
temperature profiles were measured during November
2005 and 2006 using Odyssey loggers and sensors (see
Study species and aquarium setup). Water flow was
measured using the plaster-dissolution technique of
Fulton and Bellwood (2005). This technique relates the
dissolution of spherical gypsum balls to average flow
velocity within a flume with a similar design to our flow
chambers, allowing us to reliably equate chamber and
field flow velocities. Flow measurements were taken at
all sites over a period of 10 days during November 2006,
and additionally during May/June 2006 at sites within
the lagoon. During each of 5 deployments of 24–48 hour
duration, three replicate gypsum balls were affixed to
individual stakes 15 cm above the substratum at each
monitoring site. Gypsum balls were dried to constant
mass prior to and after deployment.
During November 2005, we collected four fragments
measuring 3–4 cm diameter from 6 colonies of Acropora
nasuta and Leptoria phrygia at nine sites (these species
were absent from one site), and four fragments from 2–6
colonies of Montipora foliosa from eight sites (this
species was absent from two sites and less abundant at
all sites). In order to measure colony diameter, sampled
colonies were photographed in the field with a ruler as a
scale bar. Two fragments were immediately frozen for
subsequent analysis of total protein concentration as a
proxy for tissue biomass. Total protein was measured
using Bio-Rad’s total protein kit and protocol (Bio-Rad
Laboratories, Hercules, California, USA). Standards of
known protein concentration between 0 and 2 mg/mL
were prepared using bovine-serum-albumen (BSA,
Sigma Chemicals, Perth, Australia). We then solubilized
tissue from each fragment (two replicates per colony)
using two successive one-hour digestions in 1 mol/L
NaOH held at 908C. We then combined 100 lL of
protein solution with 5 mL of protein dye reagent and
determined absorbance at 595 nm spectophotometrically. Finally, total protein of samples was determined
by comparison with the absorbance of the calibration
standards. For the estimates of reproductive output, the
remaining fragments from each colony were fixed in 10%
formalin in seawater and later decalcified in 10% formic
acid. Reproductive output was then determined by
counting the number of eggs in each of 12–15 polyps
per fragment for Acropora nasuta and Montipora foliosa.
Corallites in Leptoria phrygia form valleys in place of
discrete polyp units. Therefore, for this species we
counted eggs contained in each of 12–15 mesenteries
(i.e., tissue containing reproductive organs). As for the
protein analyses, egg counts were conducted for two
replicate fragments per colony.
Model analyses
To predict the niche for each species, we evaluated Eq.
6 at maximum daily irradiance values between 50 and
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QUANTIFYING THE FUNDAMENTAL NICHE
773
1800 lmol photonsm2s1 and at average water flow
velocities between 2 and 40 cm/s. Subsequently, for each
species, at all combinations of light and flow conditions,
we calculated daily photosynthetic oxygen evolution
(Pday) and daily respiratory costs (Rday), and measured
the niche as the conditions where the ratio of daily
photosynthesis to respiration (P:R ratio) . 1. We
converted oxygen flux to carbon equivalents based on
molar masses, as Pday ¼ mol O2 produced12/PQ and
Rday ¼ mol O2 consumed12RQ (Anthony and Fabricius 2000) where PQ and RQ are photosynthetic and
respiratory quotients (1.1 and 0.8, respectively; Muscatine et al. 1981).
To explore the association between measured tissue
properties and energetics calculated by our model, we
predicted energy acquisition for sampled colonies based
on their diameter, average light and flow conditions at
collection sites, and the calibrated mass-transfer model
for each species. Uncertainty in parameter estimates for
mass fluxes was incorporated using a Monte Carlo
simulation technique. To do this, we made 1000 replicate
calculations of energy acquisition for each colony, using
parameters drawn randomly from multivariate Gaussian
distributions based on the variance–covariance matrices
of the fitted models. We then correlated predicted P:R
ratio (averaged over the 1000 iterations) with both
protein content and reproductive output. Spearman’s
rank correlation was used because our reproduction
data contains tied values.
RESULTS
Mass flux relationships
We observed the expected power law relationship
between Sherwood and Reynolds numbers for all species
(Fig. 1). That is, with increasing Re the degree to which
mass flux was enhanced by advection, relative to that
possible through diffusion, also increased. The rate of
increase in Sh with Re was highest for Montipora foliosa
(b ¼ 1.3), followed by Acropora nasuta and Leptoria
phrygia (b ¼ 0.85 and 0.74, respectively; see Fig. 1).
Enhancement of mass flux due to flow/colony size was
consistent across light-acclimation treatments for all
three species. Best-fit values of b (Fig. 1) for high-light
and low-light-acclimated colonies of each species were
not significantly different (t test; A. nasuta, t2,0.05 (df ¼
11) ¼ 0.9, P ¼ 0.4; L. phrygia, t2,0.05 (df ¼ 13) ¼ 1.8, P ¼
0.1; M. foliosa, t2,0.05 (df ¼ 9) ¼ 1.4, P ¼ 0.2). In other
words, we found no evidence that photoacclimatory
state influenced mass flux dynamics for our study
species.
Diffusion limitation of photosynthesis and respiration
For all three species, tissue surface oxygen concentrations were not affected by increasing colony size or
water flow velocity (Reynolds number) over at least the
upper half of the range of conditions tested (Fig. 2). The
average standing stock of O2 at the tissue surface was
always higher than ambient when colonies were
FIG. 1. Sherwood (Sh) vs. Reynolds (Re) relationship for
colonies of (A) Acropora nasuta, (B) Leptoria phrygia, and (C)
Montipora foliosa acclimated to high light levels (open symbols)
and low light levels (solid symbols). Sh indicates how strongly
mass flux is enhanced by direct transfer relative to the flux due
to diffusion (Eq. 2). Re summarizes the ratio of inertial to
viscous forces of a fluid as it moves over a surface (Eq. 1). The
coefficients a and b (Eq. 3) describe how log(Sh) scales with
log(Re), where a is the y intercept and b is the slope of this
relationship. Estimates of a and b for each species were
determined using linear regression; numbers in parentheses
are standard errors. Data are log10-transformed values calculated for individual colonies at different flow speeds. Regression
parameters are presented within panels.
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MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
Ecology, Vol. 90, No. 3
FIG. 2. Tissue surface oxygen concentration, expressed as a percentage of dissolved oxygen concentration in seawater, for
Acropora nasuta (triangles), Leptoria phrygia (circles), and Montipora foliosa (squares) during (A, C, E) photosynthesis and (B, D,
F) respiration. Data points are from measurements of maximum rates of photosynthesis (Pmax) and dark respiration (Rd) for
colonies at different flow speeds, taking into account the relationship between flow and mass transfer. Vertical dashed lines
represent Re at which oxygen concentrations are 95% of their asymptotic value. Note that all Re values have been multiplied by
104 (e.g., the number ‘‘6’’ on the x-axes represents 60 000).
photosynthesizing, and always less than ambient when
colonies were respiring. That is, the fitted parameter x
(Eq. 9) was significantly different from zero in all cases
(Table 2), with the magnitude of these differences
highest for L. phrygia followed by A. nasuta and then
M. foliosa. The value of Re at which oxygen concentration at the tissue surface reached 95% of its saturated
value (dashed lines in Fig. 2) varied between species, and
between photosynthesizing compared with respiring
colonies. For all three species, as Reynolds number
increased from low values, tissue oxygen concentrations
reached saturation more rapidly during respiration
compared with photosynthesis (aP . aR for M. foliosa
and L. phrygia; Table 2), and thus were approximately
constant over intermediate and large Reynolds numbers
(Fig. 2). This means that respiration was more sensitive
to Reynolds number when Reynolds numbers were low,
but photosynthesis was more sensitive for intermediate
and large Reynolds numbers. Although the differences
in the parameters describing the shapes of the photosynthesis and respiration responses appear small in
magnitude, such differences have a large influence on the
shape of the niche (see Discussion).
Tissue quality and reproductive output
Model predictions of daily energy acquisition were
positively correlated with tissue biomass (total protein)
and reproductive output (eggs per polyp/mesentery) for
TABLE 2. Best-fit parameter estimates describing variation in tissue oxygen concentration with increasing Reynolds number (Re)
during photosynthesis (Pmax) and respiration (Rd) for colonies of Acropora nasuta, Montipora foliosa, and Leptoria phrygia.
Pmax parameter estimate (6SE)
2
Species
R
A. nasuta
M. foliosa
L. phrygia
0.75
0.56
0.42
* P , 0.05.
Rd parameter estimate (6SE)
2
xP
aP
bP
R
6.8 (0.8)*
1.7 (0.39)*
12 (6)*
0.41 (0.07)*
0.39 (0.06)*
0.25 (0.15)
28 (3.2)*
23 (2.5)*
35 (6)*
0.73
0.79
0.74
xR
aR
bR
1.7 (0.64)*
1.4 (0.35)*
6.7 (1.9)*
0.36 (0.07)*
0.81 (0.11)*
0.35 (0.1)*
18 (1.9)*
52 (11)*
44 (6)*
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QUANTIFYING THE FUNDAMENTAL NICHE
775
FIG. 3. Correlations between predicted energy acquisition with reproductive output (no. eggs) and tissue biomass (total protein)
for colonies of Acropora nasuta (triangles), Leptoria phrygia (circles), and Montipora foliosa (squares). Spearman’s rank correlations
(R) are shown together with significance level. Points represent photosynthesis : respiration ratios averaged over 1000 Monte Carlo
simulations.
both A. nasuta and L. phrygia (Fig. 3). Conversely, for
M. foliosa, the model did not capture variation in tissue
properties observed in the field. For this species, tissue
quality varied between colonies, but this variation was
uncorrelated with energy acquisition predicted by the
model. Furthermore, the mechanistic model based on
the combined effects of light and flow on photosynthesis
and respiration was a much better overall predictor of
tissue properties than multiple regression models incorporating these two variables (Appendix B). Specifically,
multiple regressions were nonsignificant in all cases but
one, and, for the one exception, parameter estimates
indicated a negative relationship between tissue biomass
and water flow, opposite to what one would expect given
that flow enhances flux of photosynthetic gases by
reducing diffusive boundary layers.
Niche width: energy acquisition
The influence of light intensity and water flow velocity
on daily energy acquisition differed among our study
species, and was variably influenced by colony size (Fig.
4). In general, small colonies of each species had a
positive energy balance across a narrower range of
conditions than larger colonies (Fig. 4). Of the species
considered here, the niche of A. nasuta was the least
sensitive to flow and colony size. For this species, niche
size increased only slightly with colony size, due to an
increase in the range of light conditions tolerable under
high-flow conditions for large colonies (Fig. 4A, B). For
L. phrygia, the size of the niche increased with colony
size to a much greater extent (Fig. 4C, D). The model
predicted that small colonies of L. phrygia should be
restricted to habitats of high light and high flow, but for
large colonies, the region of positive energy balance
expanded to encompass regions of lower light and/or
flow. Energy balance for M. foliosa responded similarly
to an increase in colony size to that of L. phrygia, except
that larger colonies were less able to maintain positive
energy balance for combinations of high flow and low
light than small colonies were (Fig. 4E, F).
Mechanistically, the differences between species were
driven by the magnitudes of the differences in tissue
oxygen between photosynthesizing and respiring colonies. For A. nasuta, photosynthetic oxygen fluxes were
greater than respiratory fluxes at low Reynolds numbers
(Re), but the opposite was true for L. phrygia and M.
foliosa (see Table 2). This meant that colonies of A.
nasuta that were small and/or exposed to low-flow
conditions (low Re) had a positive energy balance,
whereas energy acquisition for small colonies of L.
phrygia and M. foliosa was sensitive to the relationship
between tissue oxygen and Re. Results also indicated
that, for large colonies of M. foliosa and L. phrygia,
high-flow conditions were only tolerable if irradiance
levels were also high (Fig. 4D, F). For these two species,
rates of photosynthesis at high Re were only slightly
776
MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
Ecology, Vol. 90, No. 3
FIG. 4. Energetic niche dimensions for 10 cm and 40 cm diameter colonies of: (top row) Acropora nasuta, (middle row) Leptoria
phrygia, and (bottom row) Montipora foliosa along gradients of light intensity (x-axis) and flow velocity ( y-axis. Lines represent
mean contours (solid lines) of positive energy acquisition (daily photosynthesis . respiration, represented by the shaded region of
the individual panels). Dashed lines represent 95% confidence intervals generated from 1000 Monte Carlo simulations.
greater than rates of respiration. This meant that, unless
light levels were high over most of the day, at high flow
there were some combinations of parameter values for
which energy acquisition by large colonies of M. foliosa
and L. phrygia was less than maintenance costs.
As mentioned (see Mass flux relationships above), we
found no evidence that photo-acclimatory state influenced gas diffusion dynamics for any of our study
species. However, our light-acclimation irradiances did
not include irradiances as high as those that might be
observed in shallow, clear waters on sunny days.
Therefore, to test how acclimation to high light
influenced niche boundaries, we recalculated energy
acquisition varying tissue oxygen concentrations (Pvs,
Rvs) to capture the essential components of photoacclimation to high light: namely increased oxygen
production and consumption (Appendix C). These
simulations revealed that the niche was insensitive to
light-acclimation over a broad range of parameter
combinations. However, the niche of high-light-acclimated colonies of each species was reduced, and shifted
toward the high-light/high-flow area of the environmental gradient for A. nasuta and L. phrygia and toward the
high-light/intermediate-flow region for M. foliosa (Appendix C). Overall, this indicates that the niche boundaries calculated by our model are robust over much of the
realistic range of light conditions, but may overestimate
energy acquisition in very high-light habitats.
DISCUSSION
This study establishes a quantitative link between
metabolic rates and the dominant environmental gradients on coral reefs (light intensity and water flow),
enabling prediction of the conditions under which a
positive energy balance is possible. Our analyses
demonstrate that effects of natural variation in light
and flow conditions on photosynthetic energy acquisition strongly influence tissue biomass and reproductive
March 2009
QUANTIFYING THE FUNDAMENTAL NICHE
output. In addition, the mechanistic framework developed in this study identifies important ecological
implications of variation in physiology. First, due to
its higher photosynthetic capacity, the branching coral
A. nasuta has a positive energy balance over a wider
range of conditions than L. phrygia and M. foliosa.
Second, niche size increases with colony size: larger
colonies of all three species have a positive energy
balance over a greater range of conditions because
photosynthesis is more strongly enhanced by changes in
colony size and flow (Reynolds number, Re) than
respiration is. Finally, the influence of flow on energetics
is variable across the light gradient. The synergistic
effects of light and flow influence performance in a way
that is not monotonically related to the effects of either
of these variables individually, or together. Overall,
these results indicate that the process-based model
captures nonlinear interactions between light and flow
that are unlikely to be captured with regression-based
approaches. This highlights the importance of accounting for the interaction of these variables in a mechanistic
framework.
Mass flux models
Our analyses confirm the utility of general mass flux
models (Patterson et al. 1991, Patterson 1992). An
additional advantage to such models is that, once the
mass transfer coefficient (hm, Eq. 4) has been evaluated
for a given species, the same equations can be used to
calculate the uptake and/or release of other solutes if the
solubility of these molecules is known (e.g., NO3 and
PO4; Sanford and Crawford 2000). Therefore, the
models developed here could be extended to consider
how colony growth and calcification may respond to
gradients of water flow velocity, and how changes in the
solubility of calcium and carbonate ions may affect this
process (e.g., in response to ocean acidification; HoeghGuldberg et al. 2007).
Our results reveal that small changes in the relationships between environmental gradients and different
physiological processes (e.g., photosynthesis vs. respiration) can have a strong effect on the niche. This study
indicates that respiration becomes limited by oxygen
consumption within tissue at lower Re than does oxygen
evolution during photosynthesis. In other words, flow
stops affecting respiration before it stops affecting
photosynthesis. Although the magnitude of the difference in the values of Re at which this occurs is small, the
consistency of this pattern across three species suggests
that flow enhancement of photosynthesis over a greater
range of flow conditions compared with respiration may
be a general phenomenon.
This observation could potentially be explained by
differences in the diffusion of CO2 compared with O2,
where the former substrate may be a limiting factor for
rates of photosynthesis, but not respiration (Carpenter
and Williams 2007). This highlights an underlying
assumption of the model: that efflux of oxygen during
777
photosynthesis is linearly related to influx of carbon
dioxide for carbon fixation. This assumption is supported by experimental evidence that boundary layer
resistance to different molecules decreases in parallel
with increasing flow (Koch 1994), and that flow has the
same effect on photosynthesis when measured by
changes in dissolved organic carbon compared with
dissolved oxygen (Carpenter and Williams 2007).
Therefore, boundary layer properties are not likely to
be responsible for the differences in flow effects on
respiration compared with photosynthesis. A more
likely explanation is that this phenomenon is driven by
spatial segregation in the sites of oxygen production and
respiration within tissue. During respiration, oxygen is
consumed by both coral (host) tissue as well as by
symbionts. During photosynthesis, however, oxygen
production only occurs in symbionts. Consequently, all
oxygen evolved during photosynthesis must pass
through tissue before it reaches the diffusive boundary
layer (and CO2 must diffuse likewise in reverse). The net
effect of this is an additional impediment to oxygen
efflux during photosynthesis compared with oxygen use
during respiration. This would cause photosynthesis to
reach saturation more slowly as flow increases, thereby
extending the range of flow velocities over which mass
flux is enhanced.
Predictive accuracy of the niche model
Despite a robust characterization of the physiological
mechanisms underlying photosynthetic energy acquisition for our study species, the combination of uncertainties in parameter estimates meant that the 95%
confidence interval of energy balance contours was in
some cases quite large. Niche boundary predictions
(area between the dashed lines in Fig. 4) were most
precise for A. nasuta, and most variable for small
colonies of L. phrygia and large colonies of M. foliosa.
Greater uncertainty in niche contours for the latter two
species was due to higher between-colony variance in
oxygen production and consumption (e.g., Fig. 2C–G)
and corresponding uncertainty in the parameters of the
relationships between tissue oxygen concentrations and
Re (Table 2). Despite uncertainty about the exact
position of the niche boundary, the model identifies
some important general trends. Niche size increases with
colony size; decreasing light availability leads to a
narrowing of the flow conditions that allow a positive
energy balance for large colonies of L. phyrgia and M.
foliosa; and A. nasuta has a positive energy balance over
a broader range of environmental conditions compared
to our other study species.
Energy acquisition calculated by the model proved to
be a statistically significant predictor of both tissue
biomass and reproductive output for two of the three
study species (A. nasuta and L. phrygia). However, the
model did not capture variation in these properties for
M. foliosa. We believe that there are three main reasons
why this occurred. First, M. foliosa has a laminar
778
MIA O. HOOGENBOOM AND SEAN R. CONNOLLY
growth form, and all sampled colonies grew approximately flat along the substratum. However, our field
measurements of flow velocity were taken 15 cm above
the substratum, a typical height for colonies of A. nasuta
and L. phyrgia. Due to small-scale topographical
differences, water flow at the substratum is highly
variable within sites (Carpenter and Williams 1993),
and flow over M. foliosa colonies may differ substantially from the measured values. Second, due to its
horizontal growth form, M. foliosa may be more
strongly influenced by interspecific competition (i.e., by
overtopping; e.g., Stimson 1985), or by other benthosassociated processes such as instability and bioerosion.
These factors would influence tissue quality for this
species (e.g., due to damage; Ward 1995), but are not
accounted for in the physiological model.
The third possibility is that heterotrophic feeding may
make a larger contribution to the energy budget of M.
foliosa than for our other study species. Under normal
conditions (such as those investigated here), heterotrophic feeding provides only a small fraction of total
energy acquisition (Anthony and Fabricius 2000,
Grottoli et al. 2006). Moreover, a high capacity for
heterotrophy is particularly unexpected for M. foliosa
because enhanced particle feeding is generally associated
with large polyp size (Porter 1976), whereas Montipora
have very small polyps (Veron 2000). Nevertheless,
recent experimental work indicates that heterotrophy
plays a greater role in coral energetics than previously
believed. Studies have shown that calcification rates may
be 50–75% higher in corals fed with zooplankton
(Houlbreque et al. 2003), and that overall growth rates
may double with feeding (Miller 1995). For Montipora, a
surprisingly high capacity for heterotrophy has been
demonstrated. For instance, Grottoli et al. (2006)
showed that, following a bleaching event that strongly
reduced photosynthesis, a Montipora species was able to
up-regulate heterotrophic feeding to a level that
provided sufficient carbon to meet daily maintenance
costs. A greater reliance on heterotrophic feeding would
explain the lack of agreement between model predictions
of energy acquisition and performance of Montipora
foliosa in the field.
Effect of colony size on predicted niche dimensions
For all three species considered here, the niche model
predicts that large colonies have a positive energy
balance over a broader range of light and flow
conditions than small colonies. In other words, niche
size increases with colony size, mainly through an
expansion of the tolerable flow conditions at a given
light intensity. From a physiological basis, this result is
surprising, particularly in light of the well-known
allometric scaling of metabolism with body size in many
organisms (West et al. 1997). Moreover, there is
evidence that corals reduce their rates of photosynthesis
and particle capture per unit surface area with increasing
colony size (Jokiel and Morrisey 1986, Kim and Lasker
Ecology, Vol. 90, No. 3
1998). Both of these factors indicate that the conditions
allowing a positive energy balance should decrease as
coral colonies grew larger. On the other hand, in
agreement with our findings, Elahi and Edmunds
(2007) demonstrated a positive effect of colony size on
energy balance that was driven by greater enhancement
of rates of photosynthesis compared with respiration.
Furthermore, long-term monitoring of small colonies
from several coral genera has also shown positive effects
of colony size on growth rates (Edmunds 2006).
Collectively, these findings suggest that different rules
govern the scaling of metabolism with body size for
modular compared with unitary organisms.
More broadly, these findings indicate that the
establishment of small compared to large colonies
strongly structures the habitat distributions of corals.
Although few studies have compared the conditions that
allow survival of adults and juveniles of sessile taxa (i.e.,
large compared with small coral colonies), there is some
evidence for rain forest plants that juveniles occupy a
broader range of habitats than adults (Webb and Peart
2000, Jones et al. 2007). Such a decrease in niche size
with body size for plants differs from the pattern
observed in this study. At present, we do not have
sufficient evidence to support a discussion of these
differences. However, our results suggest that energy
balance at small colony size limits species distributions,
regardless of the potential distribution of large colonies.
The niche
The utility of the niche as an ecological concept has
been a subject of debate, particularly with respect to the
various ways of measuring and defining a species’ niche
(e.g., Chase and Leibold 2003, Kearney 2006). In this
study, we have focused upon identifying the range of
physical (light and flow) conditions under which
different coral species have sufficient energy acquisition
for survival, growth, and reproduction. Undoubtedly,
biotic interactions such as competition and predation
also influence coral performance under natural conditions. However, the statistically significant correlations
between model predictions of energy balance and
measurements of tissue biomass and reproduction
indicate that such biotic factors are of secondary
importance to environmental factors as determinants
of colony condition, at least for two of our study species
(A. nasuta and L. phrygia).
Because the results presented here focus on colony
energetics and condition, they shed light on how the
energetic capacity for coral persistence, and the production of offspring, will vary along environmental gradients. However, our approach does not explicitly consider
possible effects of disturbance events (e.g., bleaching,
cyclones, and crown-of-thorns outbreaks) that can cause
high mortality of corals, nor does it account for differences in recruitment rates, both of which have potentially
large effects on local abundance. Linking environmental
gradients to corresponding gradients in species abun-
March 2009
QUANTIFYING THE FUNDAMENTAL NICHE
dance will require demographic approaches that explicitly integrate the energetic effects of environmental
gradients with estimates of species- and size-specific
mortality rates, and rates of recruitment.
Based on a mechanistic understanding of speciesspecific relationships between dominant environmental
gradients and metabolic processes, our study quantifies
the conditions that allow different coral species to
survive and reproduce. Given the current threats to
coral reefs worldwide, this framework may be particularly useful for predicting environmental tolerances of
different species. Our results highlight the importance of
incorporating organism size into physiological models
and investigations of niche size: for corals, colony size
directly influences the conditions under which a positive
energy balance is possible. In addition, this study
demonstrates the utility of process-based models for
quantifying how physiology influences ecology, and for
predicting the ecological consequences of varying
environmental conditions.
ACKNOWLEDGMENTS
We thank K. Anthony for early discussions about this work
and assistance with design of respirometry chambers. Thanks
also to R. Gegg, G. Ewels, K. Arrowsmith, and G. Reeves for
technical support, and to E. Graham, C. Glasson, M.
Magnusson, and staff of One Tree Island Research Station
for assistance with field work. This is a contribution from the
Australian Research Council Centre of Excellence for Coral
Reef Studies.
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APPENDIX A
Flow speed calibration of respirometry chambers (Ecological Archives E090-053-A1).
APPENDIX B
Multivariate regression analysis of the combined effects of light and flow on protein content and reproductive output (Ecological
Archives E090-053-A2).
APPENDIX C
Sensitivity of niche boundaries to light acclimation (Ecological Archives E090-053-A3).